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Zero : Jurnal Sains, Matematika, dan Terapan
Published by Universitas Islam Negeri Sumatera Utara
ISSN : 2580569X     EISSN : 25805754     DOI : 10.30829
Core Subject : Science, Education, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics
Arjuna Subject : -
Articles 258 Documents
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Smart Packaging from Durian Seed Starch for Real-Time Quality Classification using DenseNet-121 Putri Nutriastuti; Euis Nursaadah; Azvadennys Vasiguhamiaz; Aceng Ruyani; Nurhamidah Nurhamidah; M. Lutfi Firdaus
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28580

Abstract

This study used smart packaging made from durian seed starch with butterfly pea flower extract and applied the DenseNet121 deep learning model for real-time quality classification. Colorimetric analysis revealed a significant transition from Hue ~92° to Hue ~289° as food quality deteriorated due to pH changes. This study aimed to introduce a non-destructive vision-based classification framework utilizing the DenseNet121 deep learning architecture to demonstrate its effectiveness as a real time food freshness monitor. The dataset was divided into 70% training set, 20% validation set, and 10% test set to ensure robust model development. Performance was evaluated using accuracy, precision, recall, and F1 score. This methodology integrated physicochemical (pH) analysis with the development of a DenseNet121 architecture based Deep Learning model to classify food freshness phases. The results showed that edible films derived from durian seed starch and butterfly pea flower extract were capable of being indicators of food freshness. The dataset consisted of 160 images captured during a 12day experiment, which was expanded using stochastic data augmentation to improve model generalization. Computationally, the model achieved convergence with a training accuracy of 96% with loss 0.14 and achieved an internal testing accuracy of 0.94. Although testing on an external dataset recorded an accuracy of 0.78 due to environmental variability. This study proposes the first integration of durian seed starch based smart packaging with DenseNet121 architecture for automatic freshness classification of lempuk durian, providing a new approach for continuous food quality monitoring. These findings provide a quantitative basis for the application of applied mathematics and computer vision in sustainable food logistics.
Spatial Analysis of Indonesia’s 2019 Provincial Voter Turnout Using Geographically Weighted Regression Ernawati Pasaribu; Ega Eugenia Naomi; Arya Candra Kusuma; Firman Emmanuel Declarantius Parulian
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28152

Abstract

This study analyzes spatial heterogeneity in voter behavior using the Geographically Weighted Regression (GWR) model, estimated via GWR 4.0 software to ensure reproducibility. Using 2019 provincial-level data, we compare an OLS model against GWR to evaluate the mean voter turnout across Indonesia’s concurrent elections. The GWR model significantly improved estimation performance, evidenced by a reduction in AICc from -140.960 to -146.581 and an increase in Adjusted R² from 36.48% globally to a local range of 65.30%–82.60%. Statistical testing via F-statistic yielded a p-value of 0.0633, significant at the 10% level acceptable given the sample size (n=34). Findings reveal significant spatial non-stationarity: the Gini ratio shows a pervasive positive mobilization effect, while education and infrastructure display region-specific impacts, with infrastructure accessibility strongly influencing turnout in eastern Indonesia. These results underscore the mathematical necessity of incorporating geographic variance into predictive electoral models to capture localized socio-political dynamics.
Transmission Dynamics Model of Scabies in Islamic Boarding Schools David Prasetyo; Sunarsih Sunarsih; Sutrisno Sutrisno
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28823

Abstract

Scabies continues to pose a major public health challenge in densely populated congregate settings such as Islamic boarding schools (pesantren) in Indonesia. This study develops a stage structured - - -  compartmental model (Susceptible, Early-stage Infected, Late-stage Infected, Recovered) that explicitly accounts for differences in mite burden, infectivity, and recovery rates between early and late-stage infections. Using qualitative analysis of the nonlinear differential equations, the basic reproduction number ( ) was derived via the Next Generation Matrix method, and stability analysis was performed. Results show that the diseasefree equilibrium is locally and globally asymptotically stable when . Under baseline parameters, , with late-stage infections accounting for approximately 88.3% of total transmission. Due to the model’s assumptions of a closed population and permanent immunity, no biologically meaningful endemic equilibrium exists; once the infection runs its course, the disease eventually dies out as susceptibles are exhausted. Sensitivity analysis highlights that reducing the late-stage transmission rate ( ) and increasing the early-stage recovery rate ( ) are the most effective intervention targets. This model provides a rigorous theoretical framework to guide evidencebased scabies control strategies in high-density residential institutions.
Robustness Evaluation of ANFIS, Hybrid GA-SVM, and SVM under Controlled Time Series Structures Sri Kustiara; Kusman Sadik; Hari Wijayanto
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.29313

Abstract

This study evaluates the robustness of ANFIS, hybrid GA-SVM, and SVM under synthetic time-series structures using a factorial simulation framework combined with empirical validation. From a practical perspective, robust coal price forecasting is essential for supporting energy planning, trade management, and policy decision-making under uncertain market conditions. Empirical analysis of Indonesian coal prices reveals nonstationary behaviour, high volatility, and nonlinear dynamics. Forecasting performance is assessed using walk-forward validation, where SVM and hybrid GA-SVM demonstrate comparable accuracy and outperform ANFIS on the empirical dataset. To systematically examine model sensitivity to structural variations, a  factorial simulation design is implemented by varying seasonality, volatility, and predictor–response structure across 12 scenarios with 100 replications each. The results indicate that volatility is the most dominant factor affecting forecasting error, with significant interaction effects among structural factors. ANOVA and post hoc analysis further confirm that model performance depends more on data characteristics than on algorithmic complexity. These findings demonstrate that factorial simulation provides a systematic and robust framework for evaluating forecasting models beyond conventional empirical comparisons, while offering deeper insight into the relationship between data structure and model performance. 
Exponent and Scrambling Index of Some Composite Graphs Linna Syahputri; Saib Suwilo; Mardiningsih Mardiningsih
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28727

Abstract

A connected graphs G is primitive provided there is a positive integer k such that for each pair of vertices u and v in G there exists a uv-walk of length k. The scrambling index of a primitive graph G, , is the smallest positive integer k such that for each two vertices u and v there is a vertex w with the property that there exist a uw-walk and a vw-walk of length k. We discuss the scrambling index of the joint and the corona product of two vertex disjoint graphs. For such graphs, we discuss their primtivity and then we present their scrambling index.
The Mediation Effect of Community Empowerment and Good Governance on Forest Productivity: A GSCA Approach Bambang Hendroyono
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28957

Abstract

This study examined how transglobal leadership influenced forest productivity in Indonesia’s community plantation forest program, focusing on the mediating roles of community empowerment and governance. Secondary data from 50 regencies, selected from a population of 127, were analyzed using a Generalized Structural Component Analysis (GSCA) with the mediation testing. The results showed that leadership significantly affected community empowerment and governance, but did not directly influence forest productivity, while both mediating variables had significant positive effects on productivity. The indirect effects accounted for 77.8% of the total effect, indicating that leadership operated primarily through intermediary mechanisms. The model demonstrated satisfactory fit, with FIT = 0.612, AFIT = 0.587, and GFI = 0.93, suggesting adequate explanatory power despite the relatively small sample size. These findings indicated that improvements in forest productivity were achieved through strengthened governance systems and enhanced community capacity, highlighting the importance of integrated institutional and participatory policy interventions.
A Decision Support System for Selecting Beneficiaries of the Free Nutritious Meal Program Using Fuzzy Possibilistic C-Means M. Husen Al Farisy; M. Ivan Ariful Fathoni; Anisa Fitri; Nabila Alisa Rohmah; Siti Nur Laili Zuhri
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28831

Abstract

The Free Nutritious Meal Program (MBG) aims to improve students’ nutritional status, but beneficiary selection is challenged by data uncertainty and overlapping criteria. This study proposes a decision support system based on the Fuzzy Possibilistic C-Means (FPCM) algorithm. The dataset consists of 200 students from Bojonegoro Regency using five indicators: nutritional status, household income, family size, participation in social assistance, and distance to school. The methodology includes data standardization, clustering using Fuzzy C-Means (FCM) and FPCM, and evaluation using Partition Coefficient, Silhouette Score, and Dunn Index. The results classify students into three groups: 52% not eligible, 18.5% moderately eligible representing borderline cases requiring further verification, and 29.5% eligible. FPCM achieves higher cluster clarity with a Partition Coefficient of 0.84 compared to 0.74 in FCM, while other evaluation metrics indicate comparable structural quality between methods. These findings indicate that FPCM provides a more interpretable and robust framework for decision-making under uncertainty.
Nonlinear Ordinal Logistic Regression and Multivariate Adaptive Regression Splines (NORL-MARS) for Prediction of Diabetes Mellitus Risk Any Tsalasatul Fitriyah; Maylita Hasyim; Nur Chamidah; Toha Saifudin; Vita Fibriyani
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28733

Abstract

Diabetes Mellitus (DM) is a high-risk metabolic disease with increasing prevalence in Indonesia, requiring an effective classification model based on significant risk factors. This study uses Nonparametric Ordinal Logistic Regression based on the Multivariate Adaptive Regression Spline estimator (NOLR-MARS). Unlike conventional parametric ordinal regression, this model does not assume a fixed functional pattern but rather determines the form of the relationship based on data patterns through basis functions, making it more flexible in handling complex predictor variable interactions. Using 664 records from the Non-Alcoholic Fatty Liver Disease (NAFLD) cohort, we explore the relationship between metabolic factors, included age, sex, Body Mass Index (BMI), LDL cholesterol, and hypertension—and DM risk. This NOLR-MARS integration addresses the nonlinear relationship while maintaining the ordinal nature of DM stages, a combination often overlooked in traditional models. Based on Generalized Cross Validation (GCV) selection, the best model achieved 74.92% accuracy for in-sample data and 80.30% for out-sample data. Furthermore, a sensitivity of 70% and a specificity of 92.86% were obtained for stage 2 DM. Factors such as age, BMI, LDL cholesterol, and hypertension significantly influenced DM status. The results showed that the NORL-MARS model had good predictive performance. The novelty of this study lies in the integration of the MARS estimator into an ordinal logistic regression framework for more granular DM risk assessment. Although this model shows potential as a screening tool in high-risk metabolic cohorts, further clinical application requires external validation to ensure broader generalizability.

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